Team HiImage proposed “Receptive Field Dense UNet" based on the baseline UNet [1]}. The method is illustrated in Figure REF . The authors augmented the Receptive Field Dense (RFD) block [2]} with a channel attention module [3]}. In the first stage training, the network was trained to minimize L1 loss using Adam optimizer, learning rate 1e-4, and 256px patches as input. The model has 11M parameters, however, due to the dense connections the number of GFLOPs is high, running time is 200ms per patch in a GPU.
<FIGURE>